Anno Kurth
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ackurth.bsky.social
Anno Kurth
@ackurth.bsky.social
120 followers 540 following 2 posts
Computational Neuroscientist:Neural Circuits:Neural Data:PostDoctoral Researcher at RIKEN CBS with Toshitake Asabuki
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Reposted by Anno Kurth
neurosutras.bsky.social
BTSP in V1
www.biorxiv.org/content/10.1...
Plateau potentials are instructive signals for behavioral timescale synaptic plasticity in the neocortex
Learning occurs via the adjustment of synaptic weights across a variety of timescales. The mechanisms supporting these processes, from single-shot to iterative learning, are unclear. The prevailing mo...
www.biorxiv.org
November 20, 2025 at 11:50 PM
Reposted by Anno Kurth
engeltatiana.bsky.social
A study led by Cina Aghamohammadi is now out in ‪@natcomms.nature.com‬! We developed a mathematical framework for partitioning spiking variability, which revealed that spiking irregularity is nearly invariant for each neuron and decreases along the cortical hierarchy.
www.nature.com/articles/s41...
October 12, 2025 at 12:42 AM
Reposted by Anno Kurth
aitormg.bsky.social
Undecided which workshop to attend at #BernsteinConference ?

We put together a stellar lineup for a satellite workshop on the effects of top-down signals on neuronal dynamics! 🧠✨📣

Join us tomorrow in room 0.101 !

bernstein-network.de/bernstein-co...

With @ackurth.bsky.social
(SW2025) Top-down control of neural dynamics – Bernstein Netzwerk Computational Neuroscience
bernstein-network.de
September 28, 2025 at 3:20 PM
Reposted by Anno Kurth
jbarbosa.org
On the first workshop talk (Monday), I'll make the case that everything might be everywhere when you decode, but that is not the end of the story: different regions have very different dynamics and encoding geometries.

This workshop is organized by @aitormg.bsky.social and @ackurth.bsky.social
September 26, 2025 at 4:57 PM
Reposted by Anno Kurth
qbionc-bot.bsky.social
Jonas Oberste-Frielinghaus, Anno C. Kurth, Julian G\"oltz, Laura Kriener, Junji Ito, Mihai A. Petrovici, Sonja Gr\"un: Synchronization and semantization in deep spiking networks https://arxiv.org/abs/2508.12975 https://arxiv.org/pdf/2508.12975 https://arxiv.org/html/2508.12975
August 19, 2025 at 6:50 AM
Reposted by Anno Kurth
qbionc-bot.bsky.social
Jakob Stubenrauch, Naomi Auer, Richard Kempter, Benjamin Lindner: Stochastic synaptic dynamics under learning https://arxiv.org/abs/2508.13846 https://arxiv.org/pdf/2508.13846 https://arxiv.org/html/2508.13846
August 20, 2025 at 6:50 AM
Reposted by Anno Kurth
thetransmitter.bsky.social
Our current approach to defining neural populations is largely arbitrary. We need new methods for grouping cells, ideally by their dynamics, writes @markdhumphries.bsky.social

#neuroskyence

www.thetransmitter.org/systems-neur...
The challenge of defining a neural population
Our current approach is largely arbitrary. We need new methods for grouping cells, ideally by their dynamics.
www.thetransmitter.org
August 11, 2025 at 2:23 PM
Reposted by Anno Kurth
tyrellturing.bsky.social
1/3) This may be a very important paper, it suggests that there are no prediction error encoding neurons in sensory areas of cortex:

www.biorxiv.org/content/10.1...

I personally am a big fan of the idea that cortical regions (allo and neo) are doing sequence prediction.

But...

🧠📈 🧪
Sensory responses of visual cortical neurons are not prediction errors
Predictive coding is theorized to be a ubiquitous cortical process to explain sensory responses. It asserts that the brain continuously predicts sensory information and imposes those predictions on lo...
www.biorxiv.org
July 11, 2025 at 3:45 PM
Reposted by Anno Kurth
aitormg.bsky.social
Undecided whether to attend the #BernsteinConference2025 ?

@ackurth.bsky.social and myself put together a stellar lineup for a satellite workshop on the effects of top-down signals on neuronal dynamics! 🧠✨📣

bernstein-network.de/bernstein-co...
(SW2025) Top-down control of neural dynamics – Bernstein Netzwerk Computational Neuroscience
bernstein-network.de
July 10, 2025 at 10:52 AM
Reposted by Anno Kurth
aitormg.bsky.social
New preprint alert 📣📣📣

New results showing how strong the link between brain dynamics and structure is 💫👀🧪

"Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex" 🐾🧠
www.biorxiv.org/content/10.1...
Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex
The cerebral cortex, from sensory to higher cognitive areas, is hierarchically organised Several dynamical and anatomical measures, such as timescales and neurotransmitter receptor expression, have in...
www.biorxiv.org
May 15, 2025 at 11:36 AM
Reposted by Anno Kurth
aitormg.bsky.social
📣 New & better method to compare matrices just dropped! 📣

Ever wanted to compare things like connectivity matrices or large matrices of neuron responses in trials? There is a new way to do so in town ✨🧪

Singular Angle Similarity (SAS)
journals.aps.org/prxlife/abst...

🧵
Geometric interpretation of Singular Angle Similarity (SAS). Matrices Ma (red) and Mb (blue). Their eigenvectors scaled by the square root of their eigenvalues span the main axes of ellipsoids. These square matrices capture the correlation structure of along the horizontal and vertical axis, respectively (double-headed colored arrows). SAS compares the angles between the corresponding ellipsoids (dashed colored arrows).
May 6, 2025 at 5:49 PM
Reposted by Anno Kurth
nest-initiative.org
The NEST virtual conference is gearing up! Join us June 17-18, 2025 for a chance to exchange ideas and learn about developments in simulation science! Interested in contributing? The abstract submission deadline has been extended to **April 30**. Visit nest-simulator.org/conference. #nestsim
nest-simulator.org
April 25, 2025 at 1:07 PM
Reposted by Anno Kurth
kenmiller.bsky.social
New preprint: "The geometry of the neural state space of decisions", work by Mauro Monsalve-Mercado, buff.ly/42wVHD5. Surprising results & predictions! (Thread) We analyze neuropixel population recordings in macaque area LIP during a reaction time, random-dot motion 1/
Picture of neural manifolds for the two choices in a decision-making task, depicted in 3D and in 2D
January 31, 2025 at 2:32 PM
Reposted by Anno Kurth
mameister4.bsky.social
The unbearable slowness of being: Humans still clock in at just 10 bits/s. Even after peer review :) Share link: authors.elsevier.com/a/1kHVa3BtfH.... ArXiv: arxiv.org/abs/2408.10234
authors.elsevier.com
December 17, 2024 at 6:54 PM
Reposted by Anno Kurth
aitormg.bsky.social
DAY 10: Advent of Comp Neuro 🎄🤖🧠🧪

Modular spiking networks that are connected randomly usually fail to propagate signals, but there is a way to overcome this: topographic connectivity!

"Passing the Message: Representation Transfer in Modular Balanced Networks"
www.frontiersin.org/journals/com...
Figure 1 from Zajzon et al Schematic overview of the sequential setup and input stimuli. Networks are composed of four modules with identical internal structure, with random (A) or topographically structured (B) feed-forward projections. Structured stimuli drive specific, randomly selected sub-populations in M0. For stimulus S1, the topographic projections (B, orange arrows) between the modules are represented explicitly in addition to the corresponding stimulus-specific sub-populations (orange ellipses), whereas for S2 only the sub-populations are depicted (blue ellipses). The black feed-forward arrows depict the remaining sparse random connections from neurons that are not part of any stimulus-specific cluster. (C) Illustrative example of the input encoding scheme: a symbolic input sequence of length T (3 in this example), containing |S| different, randomly ordered stimuli (S = {S1, S2}), is encoded into a binary matrix of dimensions |S| × T. Each stimulus is then converted into a set of 800 Poissonian spike trains of fixed duration (200 ms) and rate νstim and delivered to a subset of ϵNE excitatory and ϵNI inhibitory neurons.
December 10, 2024 at 4:55 PM
Reposted by Anno Kurth
aitormg.bsky.social
DAY 6: Advent of Comp Neuro 🎄🤖🧠🧪

This nifty theory predicts how intrinsic timescales depend on network structure, neuron properties and input. A nontrivial task!

“Microscopic theory of intrinsic timescales in spiking neural networks”
tinyurl.com/43esfrrk
By @avm.bsky.social and @albada.bsky.social
Figure 1 from van Meegen and van Albada 2021: Illustration of the theory. (a) We consider populations of randomly connected neurons ( 𝛼, 𝛽) that communicate via spike trains 𝑥𝛼𝑖⁡(𝑡). The neurons of population 𝛽 are connected to those of population 𝛼 with connection probability 𝑝𝛼⁢𝛽. (b) The theory reduces a population to a single neuron driven by an effective stochastic input 𝜂𝛼. The first- and second-order statistics 𝜇𝛼𝜂 and 𝐶𝛼𝜂 of 𝜂𝛼 depend self-consistently on the output statistics, 𝜈𝛼 and 𝐶𝛼𝑥. (c) From the stationary spike train autocorrelation function 𝐶𝑥⁡(𝜏)=𝜈⁢𝛿⁡(𝜏)+ˆ𝐶𝑥⁡(𝜏), we obtain the correlation time 𝜏𝑐, the asymptotic decay 𝜏∞, and the variability of the rate across neurons, 𝜎2𝜈. (d) Instead of the stationary autocorrelation function we sometimes consider the power spectrum 𝑆𝑥⁡(𝑓), which saturates at the firing rate, 𝑆𝑥⁡(𝑓)𝑓→∞−→𝜈, and, for a renewal process, has the zero-frequency limit 𝑆𝑥⁡(𝑓)𝑓→0−→𝜈⁢CV2. Throughout, we consider the population–averaged single-unit statistics (black curve) instead of the statistics of the population-averaged activity (gray curve).
December 6, 2024 at 7:35 PM
Reposted by Anno Kurth
aitormg.bsky.social
DAY 2: Advent of Comp Neuro 🎄🤖🧠🧪

Heterogeneous connectivity brings network dynamics closer to criticality!

"Second type of criticality in the brain uncovers rich multiple-neuron dynamics"
www.pnas.org/doi/full/10....
Figure 3 from Dahmen et al: Dispersion of correlations measures stability of network dynamics. Connection strength in a sparse random network increases from top to bottom (vertical axis). (A) Connections (arrows) between a pair of observed neurons (black dots); indirect connections contribute to correlations via intermediate neurons (gray dots). (B) Bulk eigenvalues (colored dots) of the connectivity matrix in the complex plane [critical line at 1]. (C) Distributions of covariances. Enlargements are shown in Insets. (D) SD of distribution of covariances (black curve; colored symbols correspond to distributions shown in C).
December 3, 2024 at 3:44 PM
Reposted by Anno Kurth
aitormg.bsky.social
DAY 1: Advent of Comp Neuro 🎄🤖🧠🧪

Feedback in RNNs can rotate the neural subspace to separate different source signals!

"Invariant neural subspaces maintained by feedback modulation"
elifesciences.org/articles/76096
Figure 8 from Naumann et al: Schematic of the dynamic blind source separation task, context space, and the modulated feedforward network. Information flow is indicated by black arrows, and the flow of the error during training with backpropagation through time (BPTT) is shown in yellow.
December 3, 2024 at 3:41 PM